1. Field of the Invention
This invention relates to lossless image compression algorithms.
2. Description of the Prior Art
Both static graphics images or moving television images can be compressed using either the classical Shannon Information theory (Reference: C. E. Shannon, A mathematical Theory of Communications) or the newer Autosophy information theory (Reference: K. Holtz, Hyperspace storage compression for Multimedia systems, IS&T/SPIE Paper 2188-40, U.S. Pat. Nos. 4,992,868 and 4,366,551).
In a classical Shannon type image transmission, shown in FIG. 1, the required data volume or bandwidth is determined by hardware parameter, such as: rows, columns, brightness resolution and scanning rates. The image content, or what is shown on the screen, is irrelevant. In a digital image transmission the bandwidth can be calculated as the product of: pixel rows, pixel columns, colors, brightness resolution and scanning rates. The image information is scanned out, pixel by pixel, where each pixel must be separately defined in the transmission. A totally random noise pattern requires just as many bits per second for transmission as a blank screen. Any attempt of image compression, or to remove bits in the transmission, must lead to inevitable image distortions. The image distortions will increase with the compression until the image quality becomes unacceptable to the user. The only recourse is to attempt to hide these image distortions from the human observer. Shannon's information theories result in "lossy" image compression methods including the known JPEG or MPEG cosine transforms, wavelets or fractal compression.
A new Autosophy information theory was first proposed by the inventor Klaus Holtz in 1974 and first disclosed in a patent application in 1975 which later lead to U.S. Pat. No. 4,366,551. The theory is based on a prior mathematical theory of "learning" which includes six different self learning "Omni Dimensional Networks" or learning modes. An image compression scheme based on "parallel" omni dimensional networks is disclosed in U.S. Pat. No. 4,992,868. Data compression schemes based on "serial" omni dimensional networks are now used in the V.42bis compression standard which can sometimes double the transmission rates in modems. Later slight variations to the serial omni dimensional networks are known as the Lempel Ziv 78 (LZ-78) code (Reference U.S. Pat. No. Eastman 4,464,650) and the Lempel Ziv Welch (LZW) code (Reference Welch U.S. Pat. No 4,558,302). The LZW code is already used for image compression but with very disappointing results. An image compression scheme using "serial" omni dimensional networks, very similar to the present invention, was disclosed in 1995 (Reference: Klaus Holtz, Packet Video Transmission on the Information Superhighway Using Image Content Dependent Autosophy Video Compression, IS&T's 48th Annual Conference, Washington D.C., May 7, 1995) In an Autosophy type image transmission, shown in FIG. 2, the required data volume or bandwidth is determined only by the image content, such as novelty and movement in the images. The hardware parameter, such as: image size, resolution or scanning rates, become irrelevant. Autosophy information theories re-define "information" as something which is not already known by the receiver. Anything already known by the receiver is redundant and need not be transmitted again. This may lead to great "lossless" image compression which does not distort the images. Similar methods may compress still images or moving television images. The new image transmission schemes are especially suitable for the new packet switching networks, such as the Internet or ATM networks.
A conceptual autosophy television system, shown in FIG. 2 is based on the new autosophy information theory and provides an alternative to the classical Shannon methods shown in FIG. 1. The conceptual autosophy television was previously disclosed in U.S. Pat. No. Holtz 4,992,868 "True Information Television (TITV) and Vision System. The main difference between the old disclosure and the present invention is that the old system was based on "parallel" Omni Dimensional Networks while the present invention uses "serial" Omni Dimensional Networks. The autosophy information theory at present includes six different Omni Dimensional Network types or learning modes, all of which may lead to potential data and image compression applications. The known omni dimensional networks types are: serial (used in the V.42bis standard and the present invention), parallel (used for image compression in U.S. Pat. No. 4,992,868), associative (used in brain-like atuotosopher), interrelational (potential applications in languages), logical and primary. All these network types or learning modes have been disclosed in prior publications.